# Tipos de dados extras

Até agora, você tem usado tipos de dados comuns, tais como:

* `int`
* `float`
* `str`
* `bool`

Mas você também pode usar tipos de dados mais complexos.

E você ainda terá os mesmos recursos que viu até agora:

* Ótimo suporte do editor.
* Conversão de dados das requisições recebidas.
* Conversão de dados para os dados da resposta.
* Validação de dados.
* Anotação e documentação automáticas.

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship

from .database import Base


class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship

from .database import Base


class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")

database = databases.Database(DATABASE_URL)

metadata = sqlalchemy.MetaData()

notes = sqlalchemy.Table(
    "notes",
    metadata,
    sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True),
    sqlalchemy.Column("text", sqlalchemy.String),
    sqlalchemy.Column("completed", sqlalchemy.Boolean),
)


engine = sqlalchemy.create_engine(
    DATABASE_URL, connect_args={"check_same_thread": False}
)

The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models.

### Create model attributes/columns

Now create all the model (class) attributes.

Each of these attributes represents a column in its corresponding database table.

We use `Column` from SQLAlchemy as the default value.

        {
            "detail": [
                {
                    "loc": ["body", 1],
                    "msg": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
                    "type": "value_error.jsondecode",
                    "ctx": {
                        "msg": "Expecting property name enclosed in double quotes",

        {
            "detail": [
                {
                    "loc": ["body", 1],
                    "msg": "Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
                    "type": "value_error.jsondecode",
                    "ctx": {
                        "msg": "Expecting property name enclosed in double quotes",

    {!> ../../../docs_src/sql_databases/sql_app_py310/schemas.py!}
    ```

#### 🇸🇲 👗 & Pydantic 👗

👀 👈 🇸🇲 *🏷* 🔬 🔢 ⚙️ `=`, & 🚶‍♀️ 🆎 🔢 `Column`, 💖:

```Python
name = Column(String)
```

⏪ Pydantic *🏷* 📣 🆎 ⚙️ `:`, 🆕 🆎 ✍ ❕/🆎 🔑:

```Python
name: str
```

✔️ ⚫️ 🤯, 👆 🚫 🤚 😕 🕐❔ ⚙️ `=` & `:` ⏮️ 👫.

    {!> ../../../docs_src/sql_databases/sql_app/schemas.py!}
    ```

#### SQLAlchemy 风格和 Pydantic 风格

请注意，SQLAlchemy*模型*使用 `=`来定义属性，并将类型作为参数传递给`Column`，例如：

```Python
name = Column(String)
```

虽然 Pydantic*模型*使用`:` 声明类型，但新的类型注释语法/类型提示是：

```Python
name: str
```

请牢记这一点，这样您在使用`:`还是`=`时就不会感到困惑。

### 创建用于读取/返回的Pydantic*模型/模式*

{!../../../docs_src/python_types/tutorial002.py!}
```

That is not the same as declaring default values like would be with:

```Python
    first_name="john", last_name="doe"
```

It's a different thing.

We are using colons (`:`), not equals (`=`).

And adding type hints normally doesn't change what happens from what would happen without them.

But now, imagine you are again in the middle of creating that function, but with type hints.